Abstract
This paper presents the first practical implementation of Shared Object Networking (SON) for Distributed Graph Retrieval-Augmented Generation (GraphRAG), delivering a robust framework for secure, privacy-preserving, and persistent knowledge management across heterogeneous systems. By operationalizing SON’s hybrid z-axis mapping layer, our approach anchors local knowledge objects to a distributed repository of well-known entities, achieving semantic alignment without enforcing global schema uniformity.
A central innovation of this work is the integration of agentic AI components that autonomously discover, validate, and promote new knowledge—dynamically updating trust metrics and confidence intervals as evidence accumulates. Crucially, the architecture is designed with privacy and security as foundational principles: sensitive references and private objects remain local, while only abstracted summaries and trust signals are shared, ensuring rigorous data sovereignty and auditability.
Key features include:
- Practical Privacy and Security: Explicit mechanisms ensure that private data is never exposed, with all knowledge sharing governed by reference-only exchanges and transparent provenance tracking.
- Persistent, User-Focused Knowledge Framework: The system decouples core knowledge from inference, enabling continuous post-training updates and autonomous adaptation to evolving user needs—without retraining foundational models.
- Transparent Trust and Provenance: Every knowledge object and inference is tracked for provenance and trust, empowering users and systems to audit information sources and validation steps.
- Hallucination Reduction: By enforcing evidence-based confidence thresholds and grounding generation in verifiable, distributed knowledge, the risk of unsupported outputs is minimized.
This implementation demonstrates how SON-based distributed GraphRAG establishes a new paradigm for persistent, explainable, and user-centric AI—laying the groundwork for the next generation of adaptive, trustworthy knowledge systems.